found that high-dimensional quantitative CT texture analysis was potentially able to identify ccRCC with and without PBRM1 mutations using the artificial neural network (ANN) and random forest (RF) algorithms as machine learning classifiers; RF outperformed ANN in that study (95.0% vs 88.2%) (104). This evidence concerns the gene PBRM1 and nonpapillary renal cell carcinoma.